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|Title:||A Theory for Dynamic Weighting in Monte Carlo Computation|
|Citation:||Liu, J.S.,Liang, F.,Wong, W.H. (2001-06). A Theory for Dynamic Weighting in Monte Carlo Computation. Journal of the American Statistical Association 96 (454) : 561-573. ScholarBank@NUS Repository.|
|Abstract:||This article provides a first theoretical analysis of a new Monte Carlo approach, the dynamic weighting algorithm, proposed recently by Wong and Liang. In dynamic weighting Monte Carlo, one augments the original state space of interest by a weighting factor, which allows the resulting Markov chain to move more freely and to escape from local modes. It uses a new invariance principle to guide the construction of transition rules. We analyze the behavior of the weights resulting from such a process and provide detailed recommendations on how to use these weights properly. Our recommendations are supported by a renewal theory-type analysis. Our theoretical investigations are further demonstrated by a simulation study and applications in neural network training and Ising model simulations.|
|Source Title:||Journal of the American Statistical Association|
|Appears in Collections:||Staff Publications|
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